Computational localization of fibrillation sources

ABSTRACT

A system for computational localization of fibrillation sources is provided. In some implementations, the system performs operations comprising generating a representation of electrical activation of a patient&#39;s heart and comparing, based on correlation, the generated representation against one or more stored representations of hearts to identify at least one matched representation of a heart. The operations can further comprise generating, based on the at least one matched representation, a computational model for the patient&#39;s heart, wherein the computational model includes an illustration of one or more fibrillation sources in the patient&#39;s heart. Additionally, the operations can comprise displaying, via a user interface, at least a portion of the computational model. Related systems, methods, and articles of manufacture are also described.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation application of U.S. patentapplication Ser. No. 15/389,245, filed on Dec. 22, 2016, entitled“COMPUTATIONAL LOCALIZATION OF FIBRILLATION SOURCES,” which claimspriority to U.S. Provisional Patent Application No. 62/271,113, filed onDec. 22, 2015, and entitled “COMPUTATIONAL LOCALIZATION OF VENTRICULARFIBRILLATION SOURCES,” the disclosures of these applications areincorporated herein by reference.

TECHNICAL FIELD

The subject matter described herein relates to computationallocalization of fibrillation sources, and more particularly,identification of ventricular fibrillation (VF) and/or atrialfibrillation (AF) sources.

BACKGROUND

Ventricular fibrillation (VF) and atrial fibrillation (AF) can causesymptoms, morbidity (syncope or stroke), and mortality. Stableelectrical rotors, recurring electrical focal sources, and othermechanisms are important drivers of sustained and/or clinicallysignificant episodes of VF/AF. In order to treat VF/AF, therapeuticablation, radiofrequency (RF), cryogenic, ultrasound, and/or externalradiation sources can be used to target and/or eliminate thesemechanisms. The ability to map the sustaining mechanisms for VF and/orAF using noninvasive methods would provide significant benefit in themanagement of such arrhythmias.

Current methods for reliably identifying the location ofVF/AF-sustaining mechanisms (e.g., rotors or focal sources) arepresently suboptimal. They often require an invasive procedure, theinsertion of expensive 64-electrode basket catheters, and/or mappingarrhythmias using an expensive, difficult to obtain/manufacture, andcumbersome body surface vest, which may interfere with the placement ofdefibrillator pads. Therefore, such procedures are expensive, timeconsuming, and potentially hazardous to patients. Accordingly, methodsfor identifying the existence and/or location of fibrillation sourceswith less costly and/or less invasive procedures may provide significantclinical benefit.

SUMMARY

In some aspects, a method, computer program product and system areprovided. In an implementation, a system for computational localizationof fibrillation sources is provided. The system can include (orotherwise utilize) at least one processor and/or memory, which can beconfigured to perform operations including generating a representationof electrical activation of a patient's heart and comparing, based oncorrelation, the generated representation against one or more storedrepresentations of hearts to identify at least one matchedrepresentation of a heart. The operations can further comprisegenerating, based on the at least one matched representation, acomputational model for the patient's heart, wherein the computationalmodel includes an illustration of one or more fibrillation sources inthe patient's heart. Additionally, the operations can comprisedisplaying, via a user interface, at least a portion of thecomputational model.

In some aspects, the generated representation and/or the one or morestored representations can include three-dimensional data (e.g., can be3D models). In some aspect, the generated representation and/or the oneor more stored representations can comprise vectorcardiograms. Comparingthe representations can include determining, for each of the one or morestored representations, a correlation factor between the generatedrepresentation and the stored representation, and the at least onematched representation can be identified as the stored representation(s)with a highest correlation factor.

In some variations, the operations can further comprise generating aplurality of computational heart models with varying shapes, geometries,fiber orientations, scars, fibrillation source types, and fibrillationsource locations. The operations can also comprise filtering theplurality of computational heart models based on a shape or scarring ofthe patient's heart to identify a filtered set, and/or selecting the oneor more stored representations for the comparing based on the filteredset. In various implementations, the filtering can be based oncomputerized tomography imaging data, magnetic resonance imaging data,echocardiography data, X-ray data, fluoroscopy data, and/or the like.

In some variations, the computational model comprises one or more of aleft atrium, a right atrium, a left ventricle, and a right ventricle,and/or the one or more fibrillation sources can be mapped to one or moreof the left atrium, the right atrium, the left ventricle, and the rightventricle. The one or more fibrillation sources can include a rotor or afocal source, or some other source of ventricular fibrillation or atrialfibrillation. In some variations, the computational model includes athree-dimensional mesh in a heart shape and/or a finite statefibrillatory source map of cardiac electrical activations mapped to themesh.

In some variations, the operations can further comprise generatingelectrocardiogram plots based on the patient's heart, and/or generatingthe vectorcardiogram based on the electrocardiogram plots. In somevariations, the operations can further comprise generating a secondcomputational model for the patient's heart based on the (original)computational model, wherein the second computational model is generatedto include a number of fibrillation sources that is less than the one ormore fibrillation sources. A side-by-side comparison of thecomputational model and the second computational mode can be displayedvia a user interface. In some aspects, the second computational modelcan be generated by removing one of the one or more fibrillation sourcesfrom the computational model. In some variations, the operations canfurther include determining a change in fibrillation between thecomputational model and the second computational model.

Implementations of the current subject matter can include systems andmethods consistent with the present description, including one or morefeatures as described, as well as articles that comprise a tangiblyembodied machine-readable medium operable to cause one or more machines(e.g., computers, etc.) to result in operations described herein.Similarly, computer systems are also described that may include one ormore processors and one or more memories coupled to the one or moreprocessors. A memory, which can include a computer-readable storagemedium, may include, encode, store, or the like one or more programsthat cause one or more processors to perform one or more of theoperations described herein. Computer implemented methods consistentwith one or more implementations of the current subject matter can beimplemented by one or more data processors residing in a singlecomputing system or multiple computing systems. Such multiple computingsystems can be connected and can exchange data and/or commands or otherinstructions or the like via one or more connections, including but notlimited to a connection over a network (e.g. the Internet, a wirelesswide area network, a local area network, a wide area network, a wirednetwork, or the like), via a direct connection between one or more ofthe multiple computing systems, etc.

The details of one or more variations of the subject matter describedherein are set forth in the accompanying drawings and the descriptionbelow. Other features and advantages of the subject matter describedherein will be apparent from the description and drawings, and from theclaims. While certain features of the currently disclosed subject matterare described for illustrative purposes in relation to an enterpriseresource software system or other business software solution orarchitecture, it should be readily understood that such features are notintended to be limiting. The claims that follow this disclosure areintended to define the scope of the protected subject matter.

DESCRIPTION OF DRAWINGS

The patent or application file contains at least one drawing executed incolor. Copies of this patent or patent application publication withcolor drawing(s) will be provided by the Office upon request and paymentof the necessary fee.

The accompanying drawings, which are incorporated in and constitute apart of this specification, show certain aspects of the subject matterdisclosed herein and, together with the description, help explain someof the principles associated with the disclosed implementations. In thedrawings,

FIG. 1 depicts a block diagram of a system for computationallocalization of fibrillation sources, in accordance with some exampleimplementations;

FIG. 2A depicts a map of activation time in a patient's right atrium, inaccordance with some example implementations;

FIG. 2B depicts a map of activation time in a patient's left atrium, inaccordance with some example implementations;

FIG. 2C depicts a fibrillatory source map for VF, in accordance withsome example implementations;

FIG. 2D depicts a fibrillatory source map for AF, in accordance withsome example implementations;

FIG. 3A depicts a biventricular computational model of an electricalrotor in the right ventricle, in accordance with some exampleimplementations;

FIG. 3B depicts a biventricular computational model with an identifiedrotor, in accordance with some example implementations;

FIG. 4A depicts a graph of electrocardiogram (EKG) data computed from acomputational model, in accordance with some example implementations;

FIG. 4B depicts vectorcardiograms (VCG) computed from a computationalmodel, in accordance with some example implementations;

FIG. 5A depicts an endocardial, isochronal map of cardiac activationduring VF in a patient's left ventricle, in accordance with some exampleimplementations;

FIG. 5B depicts a VF source location within the patient's anatomy,according to endocardial voltage, in accordance with some exampleimplementations;

FIG. 6A depicts a graph of EKG readings during human VF, in accordancewith some example implementations;

FIG. 6B depicts a VCG derived from recorded EKG data, in accordance withsome example implementations;

FIG. 7A depicts a biventricular computational model with VF at differenttime points, in accordance with some example implementations;

FIG. 7B depicts a graph of computed EKG tracings over a time interval,in accordance with some example implementations;

FIG. 7C depicts computed VCG tracings over the time interval, inaccordance with some example implementations;

FIG. 8A depicts a computational model of the right and left atria, inaccordance with some example implementations;

FIG. 8B depicts a computational model of the right and left atria with asimulated diseased substrate which maintains an identified rotor, inaccordance with some example implementations;

FIG. 9 depicts a block diagram of an example computing apparatus, inaccordance with some example implementations;

FIG. 10 depicts an example of a method for computational localization offibrillation sources, in accordance with some example implementations;

FIG. 11 depicts an example of a method for computing the effect ofablation and a subsequent risk of cardiac fibrillation, in accordancewith some example implementations; and

FIG. 12 depicts a simulated VCG and a measured human VCG of a rotor inthe same anatomical position, in accordance with some exampleimplementations.

When practical, similar reference numbers denote similar structures,features, or elements.

DETAILED DESCRIPTION

As noted above, it can be desirable to identify the existence and/orlocation of fibrillation sources with less costly and/or less invasiveprocedures/methods. Accordingly, non-invasive systems and methods foridentifying the presence and/or location of rotors or focal sources(collectively referred to herein as “fibrillation mechanisms”) inpatients with ventricular fibrillation (VF) or atrial fibrillation (AF)are described. In some implementations, readily-available twelve-leadelectrocardiogram (EKG) sensor devices can be applied to the surface ofa patient's skin, instead of single-use 64-electrode basket cathetersthat require surgical implantation and/or external electrocardiogram(“EKG” or “ECG”) vests using twenty or more electrodes, which are highlyspecialized and costly.

FIG. 1 illustrates a functional block diagram of a system 100 in whichfeatures consistent with the described subject matter may beimplemented. As illustrated, the system 100 can include a computingsystem 110 capable of communicating with one or more user access devices140 and/or one or more sensor devices 150A-D (collectively referred toas sensor devices 150). In some aspects, the computing system 100 canutilize one or more interfaces 118 for communication. Communicationamong the devices in the system 100 can be through the use of directcommunications, such as through the use of a wireless connection likeBluetooth, near-field communication (NFC), ZigBee, WiFi, somecombination thereof, and/or the like. Additionally or alternatively,communication among the devices in the system 100 can be through the useof a hard wired connection, such as universal serial bus (USB) and/orthe like. Communication can additionally or alternatively occur throughindirect communications, such as over a network 160, which can include alocal area network, a wide area network, a wireless network, theInternet, some combination thereof, and/or the like.

Communication over the network 160 can utilize a network access device165, such as a base station, a Node B, an evolved Node B (eNB), anaccess nodes (ANs), a hotspot, and/or the like. In some aspects, any ofthe user access devices 140 can include personal computers, desktopcomputers, laptops, workstations, cell phones, digital media devices,smart phones, smart watches, PDAs (personal digital assistants),tablets, hardware/software servers, sensors, sensor devices, terminals,access terminals (ATs), mobile stations, user equipment (UE), subscriberunits, and/or the like. In some aspects, any of the sensor devices 150can include EKG sensors/devices, vectorcardiogram (VCG) sensors/devices,heart imaging devices, and/or the like. In some implementations, heartimaging devices can include one or more of computerized tomography (CTor CAT) scan devices, magnetic resonance imaging (MRI) scan devices,sestamibi scan devices, thallium scan devices, multi-gated acquisitionscan devices, X-ray devices, echocardiography devices, fluoroscopydevices, and/or the like. In various implementations, data (e.g., heartimaging data) can be provided and/or a respective device (e.g., heartimaging device) may not be present. Wired or wireless communicationamong the computing system 110, user access devices 140, and/or sensordevices 150 can occur according to various protocols and/or accesstechnologies (e.g., Global System for Mobile Communication (GSM),Universal Mobile Telecommunications System (UMTS), technologiesdeveloped by IEEE such as WiFi and/or Bluetooth, technologies developedby the Third Generation Partnership Project (3GPP) or 3GPP2 such as LongTerm Evolution (LTE) and/or CDMA2000, etc.).

At least a portion of the illustrated system 100 may include hardwareand/or software that interacts with stored data, models, algorithms,etc., and/or receives, transmits, defines, creates, and/or updates data.As illustrated, the computing system 110 can include a processor 190,which can be used to manage/control the operation of the computingsystem 110. As further illustrated, the computing system 110 can includecore software 112 and/or one or more software modules 114. The coresoftware 112 can provide one or more features of a high-levelprogramming software system. The software modules 114 can provide morespecialized functionality. For example, the core software 112 and/orsoftware modules 114 can include sensor management and/or dataprocessing functionality. In some aspects, the core software 112 orother similar software/hardware can be capable of accessing a databaselayer, such as the database 120. The database 120 can store any kind ofdata, potentially including but not limited to data retrieved fromsensor devices 150, computational models, EKG data, VCG data, machinelearning algorithms, data transformation algorithms, and/or the like.

For example, as illustrated, the database 120 can include a modellibrary 192, patient-specific models 194, a patient data library 195, aVCG library 196, data transformation algorithms 197, and/or machinelearning algorithms 198. The model library 192 can hold a plurality ofcomputational models of hearts, portions of hearts, other organs, and/orthe like. The computational models in the model library 192 can behigh-resolution (e.g., greater than about 500,000 degrees of freedom)finite element models. At least a portion of the computational modelscan be generated through incorporating the Fenton-Karma,Bueno-Cherry-Fenton, ten Tussher-Noble, or a similar, detailed ionicmodel of the human ventricular or atrial action potential duringsimulated VF or AF, respectively.

The patient-specific models 194 can include computational models similarto the models in the model library 192, but the models in thepatient-specific models 194 can be generated based upon data from actualpatients. For example, as illustrated, an endocardial sensor device 150Acan be applied to record data from the interior of a heart in a patient130A. At the same (or approximately the same) time, an EKG sensor 150Bcan be applied to the exterior of the patient 130A to record EKGreadings. Based upon the combination of these readings, one or morepatient-specific models 194 can be generated and stored. In someimplementations, patient-specific models 194 can additionally oralternatively be based upon CT scan data, MRI scan data, sestamibi scandata, thallium scan data, multi-gated acquisition scan data, fluoroscopydata, x-ray data, echocardiography data, and/or other cardiac imagingdata, which can be used to identify the shape, scarring, etc. of theheart of the patient 130N.

The VCG library 196 can include VCG data for the computational models ofthe model library 192 and/or the patient-specific models 194. Forexample, VCG models can be simulated based on the computational modelsin the model library 192 (e.g., based upon EKG data associated with eachmodel). In some implementations, VCG models can includethree-dimensional tracings of electrical activity in a heart or someportion thereof. In some aspects, the VCG data can includetemporospatial VCG data. The VCG data in the VCG library 196 may serveas diagnostic templates against which VCGs constructed from patient datacan be matched. For example, this VCG library data can be compared withVCG data from a patient to identify the location(s) of VF/AF sourceswithin the patient. For example, as illustrated, the EKG sensor device150D can obtain EKG data from patient 130N. This EKG data can be used togenerate VCG data, and the generated VCG data can be compared againstthe VCG data from the VCG library 196. Based upon the level ofcorrelation among these data sets, VF/AF sources can be identified, asdescribed herein. In some implementations, VCG data from the VCG library196 can be (pre)filtered for comparison based upon physicalcharacteristics (e.g., shape, scars, etc.) of the heart of the patient130N, which can be determined based upon data obtained through a cardiacimaging device 150C, which can include a CT scan device, an MRI scandevice, an echocardiography device, and/or the like. In someimplementations, the data within the VCG library 196 canadditionally/alternatively be stored within the model library 192. Forexample, each computational heart model can include or be associatedwith VCG data.

In some implementations, the computational models within the modellibrary 192 can be generated and/or verified based upon the patientspecific models 194. For example, VCGs in the VCG library 196 may bevalidated by comparing them with human VCGs, which can be obtained fromthe patient-specific models 194. For example, FIG. 12 shows a comparisonof VCGs 1220, 1260, generated based on a human rotor 1210 and asimulated rotor 1250, from the same ventricular location. In someaspects, the human rotor 1210 can be a VF source in a patient. Asillustrated, the rotor can be in a mid-ventricular, postero-lateralposition within the left ventricle, and/or rotating in acounterclockwise direction when viewed from the endocardium. Thecorresponding first VCG 1220 illustrates a clinical VCG loop for thispatient, for a particular cycle of VF.

In some aspects, the second VCG 1260 illustrates a simulated VCG loop,which closely approximates the clinical VCG 1220 (e.g., for the samecycle of VF). The second VCG 1260 can be stored within and/or retrievedfrom the VCG library 196. The corresponding computer-simulated rotor1250 can simulate the VF source indicated by the data from the VCG 1260.As illustrated, the location of the simulated VF source rotor 1250 canalso be located in the mid-ventricular, postero-lateral position withinthe left ventricle, and/or rotate in a counterclockwise direction whenviewed from the endocardium. As illustrated, the VCGs 1220, 1260 arecolor-coded based on timing information.

Referring back to FIG. 1, in some aspects, the VCG in thepatient-specific models 194 can be constructed from an existing humandata set of surface EKGs, which can be stored in and/or provided fromthe patient data library 195, with known rotor and focal sourcelocations identified using concurrent electrode invasive endocardialrecordings taken from patients with induced VF.

In some aspects, the robustness of the patient specific models 194 canincrease the accuracy and/or value of the model library 192, which canincrease the likelihood of identifying VF/AF sources within a patient130 and/or the accuracy of determining the specific location of theVF/AF sources within a heart of a patient 130. The machine-learningalgorithms 198 can be trained based upon the patient-specific models 194to generate algorithms for detecting VF/AF within a patient based uponreceiving EKG sensor data, CT scan data, VCGs, and/or the like. Forexample, correlated EKG data and VCGs may be used to trainmachine-learning algorithms to identify VF/AF mechanisms and loci. Insome example embodiments, an algorithm for deriving diagnostic criteriato predict the presence and location of VF/AF rotors can be provided. Tothis end, automated diagnostic tools may be provided to automaticallycompare VCGs computed from surface EKGs of patients with VF/AF againstdiagnostic templates (e.g., the VCG library 196) to identify the VF/AFmechanism(s) and location(s) using statistical classification and/ormachine learning techniques.

In some implementations, data transformation algorithms 197 can be usedto standardize EKG and/or VCG data measured from a patient 130A toaccount for inter-patient differences in thoracic dimensions, lunggeometry, extent of musculature, body fat composition, and/or otherfactors which may affect surface electrode measurements, usingstatistical classification and/or machine learning techniques.

In some implementations, the model library 192, the patient-specificmodels 194, the VCG library 196, the machine learning algorithms 198,the data transformation algorithms 197, and/or the patient data library195 can be specific to a VF/AF mechanism. For example, the model library192 and the patient-specific models 194 can include models for rotorsand separate models for focal sources. Accordingly, the VCG library 196can include VCGs based on rotors and separate VCGs based on focalsources. As rotors and focal sources materialize in different wayswithin VCGs, using separate sets of models/VCGs can be used to identifythe mechanism, in addition to its location.

In some aspects, the core software 112 can be configured to load theinformation from the database 120 to memory 116 (e.g., main memory) inresponse to receipt of a instructions, data, or a query initiated by auser or computer system through one or more sensor devices 150, useraccess devices 140, and/or the like. Although the database 120 isillustrated as being located within the computing system, in variousimplementations, at least a portion of the database 120 can be separatefrom the computing system 110.

In some aspects, one or more of the software modules 114 can beconfigured to utilize data stored in the memory 116, data stored in thedatabase 120, and/or data otherwise accessible to the computing system110. In some aspects, the computing system 110 can be capable ofutilizing external software, which can provide additionalfunctionalities or services which may not be available at the computingsystem 110. In some aspects, the external software may include cloudservices. In some aspects, the computing system 110 can aggregate orotherwise provide a gateway via which users can access functionalityprovided by external software. In some implementations, the database 120and/or the contents thereof can be located across one or more servers,and/or communication among the computing system 110, the user accessdevices 140, and/or the sensor devices 150 can occur over the network160.

In some aspects, the database 120 may be physically stored in a hardwareserver or across a plurality of hardware servers. In some aspects, thesystem 100 may be implemented as a cloud-based system and/or a dataprocessing system.

FIG. 2A depicts a fibrillatory source activation map 200 (inmilliseconds according to the colored scale) in a patient's right atrium210, in accordance with some example implementations. As illustrated,longer and/or more sustained activation of the right atrium 210 canrotate around a source 220. This source 220 can be regarded as a sourceof AF, and more particularly, a rotor. In some aspects, a rotor can beregarded as an organizing center for AF that is at least partiallyrotational in nature (e.g., an area in which the duration of activationtime is the greatest). Although the right atrium 210 is illustrated anddescribed, rotors can exist within any chamber of the heart.

FIG. 2B depicts a fibrillatory source activation map 250 (inmilliseconds according to the colored scale) in a patient's left atrium260, in accordance with some example implementations. As illustrated,longer and/or more sustained activation of the left atrium 260 canemanate from a source 270. This source 270 can be regarded as a sourceof AF, and more particularly, a focal source. In some aspects, a focalsource can be regarded as an organizing center from which AF isgenerated distally (e.g., an area from which the greatest duration ofactivation time emanates). Although the left atrium 270 is illustratedand described, focal sources can exist within any chamber of the heart.

FIG. 2C depicts a fibrillatory source map 280 for VF, in accordance withsome example implementations. In some aspects, the fibrillatory sourcemap 280 can be a product of the computer algorithms described herein. Asillustrated, areas within the heart which maintain VF are color-codedaccording to the percent of VF cycles at which the VF source 285 islocated at specific areas of the ventricle.

FIG. 2D depicts a fibrillatory source map 290 for AF, in accordance withsome example implementations. In some aspects, the fibrillatory sourcemap 290 can be a product of the computer algorithms described herein. Asillustrated, areas within the heart which maintain AF are color-codedaccording to the percent of AF cycles at which the AF source 295 islocated at specific areas of the atrium.

Identification of the location of the source(s) 220, 270, 285, and 295of VF/AF can be beneficial, as the knowledge of its location can helpguide surgical procedures and minimize the amount of guesswork requiredby medical professionals. For example, in a patient with AF, individualrotors (e.g., as illustrated in FIG. 2A) or focal sources (e.g., asillustrated in FIG. 2B) can be targeted for ablation. Additionally,sources can be prioritized according to percentages of cycles/timeemanating from such sources (e.g., as illustrated in FIGS. 2C and 2D).Ablation can then be delivered to areas which sustain the clinicalarrhythmia rather than delivered to non-source tissue. In turn, thechances of the success of the procedure can be increase, as the locationof the source(s) 220, 270 can be targeted specifically. Additionally,there can be a lower risk of damage to other organs, nerves, bones,muscles, etc. during surgery, decreased recovery time, and/or minimalscarring.

Heart failure is a complex disease that may involve and/or be based uponchanges in ventricular shape, fiber orientation, ion channel expressionremodeling, and/or other conditions/changes as well. Thus, computationalmodels of the heart may be generated, modified, displayed, or otherwiseutilized to identify a source/location of VF/AF based upon theconditions/changes of a specific patient according to the computingsystem 110 of FIG. 1, for example.

Computational models of the heart (or portions thereof) can be generatedbased upon imaging information (e.g., three-dimensional) representativeof a heart and/or electrical data representative of an electrophysiologyof the heart. At least some of the computational models may bepatient-specific in the sense that the data used to generate thecomputational model and the parameters/metrics used in connection withthe computational model may be specific to a given patient, takingconditions/changes into account. However, the time, effort, cost, andinvasive procedures required to generate a patient-specific model can beprohibitive. For example, in order to measure electrical properties fromwithin a heart, the positioning of an endocardial recording catheter ordevice may be required. Accordingly, at least some of the computationalmodel may be general models in the sense that they are notrepresentations of a specific patient's heart. These computationalmodels can be generated manually, based upon patient-specific models orother patient-specific data, based upon measured/known characteristicsof the human heart, and/or the like. Generated models can then be“transformed” to more specific patient configurations, as noted in thecomputing system 110 of FIG. 1, for example.

The general models can be modified to generate additional generalmodels. For example, based upon a general model that has been verifiedas sufficiently accurate (e.g., through comparison to patient-specificmodels or other data obtained from a patient), the shape of the heartand/or location of the scars within the heart can be altered. As theshape and/or scarring of a heart can affect VF/AF, having additionalmodels with varying hear shapes/scars can provide for additionalaccuracy in locating a source of VF/AF. In some implementations,thousands of computational models can be generated and/or stored withina model library 192. Any of these generation techniques can be manual,at least partially automated, and/or based on machine learning. In someaspects, the computational models can be regarded as finite elementcomputational models of cardiac arrhythmia.

Measurements of a patient's cardiac electrical properties may begenerated and/or received. For example, the computing system 100 mayreceive and/or record a patient's EKG data. In some implementations, theEKG data may be obtained from an EKG sensor device, such as 12-lead EKG,that records the continuous, dynamic signals of cardiac electricalfunction from multiple body locations (e.g., on the surface of thechest, arms, legs, head, etc.) of the patient. Additionally, thecomputing system 100 may receive and/or record a patient's endocardialdata. In some implementations, the endocardial data can be obtained froma steerable mapping and/or ablation catheter located within at least onechamber of the heart (e.g. an ablation catheter located in the leftand/or right ventricular for VF, or an ablation catheter within the leftand/or right atria for atrial fibrillation), or from one or morebasket-catheters, such as a 64-electrode basket catheter located withinat least one chamber of the heart (e.g., one in the left ventricle andone in the right ventricle for VF, or one in the left atrium and on inthe right atrium for AF), that records the continuous, dynamic signalsof cardiac electrical function from within the heart of the patient.

In some aspects, the endocardial data can be matched (e.g., temporally)with EKG data, such that the computing system 100 has access to datarepresentative cardiac electrical function from the interior andexterior of a patient's heart. Based upon the relationships betweenendocardial and EKG data, patterns can be identified and/or correlationscan be defined in order to identify VF/AF sources (e.g., rotors or focalsources). In some implementations, endocardial and EKG data can bemeasured when a patient's heart is excited and/or when a patient's heartis in a relaxed state. In an example implementation, the heart'selectrical activity can be recorded from routine pacing within the heartat one or more locations to establish the relationship between cardiacactivation and surface EKG recordings and/or computed VCGs. In anotherimplementation, VF/AF can be induced within a patient, and theelectrical data during this time can be measured/recorded. As the datademonstrates how a patient's heart acts during VF/AF, it can be comparedagainst baseline data and data from other patients to identify VF/AFsources.

Imaging data of a patient's heart (e.g., left atrium, right atrium, leftventricle, and/or right ventricle) may be received. For example, acomputing system may receive and/or record image data that includesheart images (or portions thereof) obtained from a clinical cardiac CTscan device, 2D or 3D echocardiography devices, a myocardial perfusionscan device, an Mill device, a positron-emission-tomography device, anX-ray device, a fluoroscopy device, and/or other devices capable ofgenerating or providing images of a heart (and/or portions thereof). Tocombine the 3D anatomic model of the heart with the endocardial and/orEKG data, the data processor may register (e.g., align) the data so thatthe endocardial and/or EKG data is aligned with the properorientation(s) of the heart (e.g., of ventricle or atrial regionsthereof).

A four-dimensional (4D) patient-specific computational model with dataon a patient's electrical activity may be generated. For example, thecomputing system 110 may generate a 4D model based on 3D data receivedwith the added dynamics from the electrical activity data. The 4Dcomputational model may provide a 3D representation of the morphologyand anatomy of the heart (or portions thereof) over time, and canprovide time-varying electrical dynamics of the heart (or portionsthereof), such as time-varying EKG and/or endocardial data. Theelectrical dynamics may include the activation patterns and/or theelectrical trajectories of the activations through the myocardium. Theelectrical dynamics can include patterns (for example, sequences) ofelectrical repolarization/recovery. The model may also includeadditional/alternative aspects, such as the regional distribution ofperfusion or infarction, which may be measured in individual patients orsimulated.

In some implementations, a computational model may include EKG dataoverlaid and/or registered on 3D biventricular geometry of the patient'sheart, the human fiber architecture of the heart, region(s) ofheterogeneous conductivities caused by the presence of myocardialischemia, infarction(s), anatomic (and/or functional) electricalconduction defects, such as partial and/or complete bundle branch block,and/or the like. The models can be generated using finite elementmeshes. Patient-specific finite element meshes of the heart (which mayinclude its ventricular anatomy) may be generated from image data, suchas clinical CT data, perfusion images, MRI data, and/or other types ofimage data.

The computational model may also include a heart's fiber architecture.The heart's fiber architecture may be estimated empirically using, forexample, a log-Euclidean interpolation framework for registering DT-MRmeasurements to the anatomical models. Reconstructed diffusion tensorsmay be fitted as a field of log-transformed components in acorresponding anatomical mesh to interpolate local fiber, sheet, andsheet-normal axes. The fiber orientations in the resulting model may bemapped to a patient via large-deformation diffeomorphic mapping andreoriented based on the 3D deformation gradients between the templateand target patient ventricular geometries to account for the effect ofventricular shape differences on fiber orientation. The resultingfiber-sheet model forms the local basis of transversely isotropic ororthotropic ventricular electrical conductivity (which may have afiber-sheet anisotropy ratio of about 7:1 for example).

The computational model may also include regions of myocardial ischemia,infarction, and/or other like regions. When this is the case, myocardialischemic or infarcted regions may be identified from, for example,perfusion images and/or sestamibi perfusion images obtained duringstress and rest. The myocardial ischemia or infarction boundary regionsmay be demarcated on the generated anatomical meshes of the heart. Forexample, a patient may have a posteroseptal infarction, and may have aninferior infarction. These regions may be registered in thecomputational model as a binary field of normal and infarcted tissue.

The computational model may also include myocardial electricalconductance properties, such as electrical conductivity of the leftventricular and right ventricular endocardial or bulk myocardial tissuein the muscle fiber and transverse orientations as well asconductivities in the borderzone and/or infarcted or ischemic regions.The potentials may be described by a model of human ventricular myocytesmodified to accommodate changes in channel kinetics occurring duringheart failure. Action potential propagation may be modeled in amono-domain or bi-domain reaction-diffusion mathematical framework.Electrical conductivity in the ventricular domain may be partitionedinto left ventricle and right ventricle sub-endocardial regions (forexample, ˜3 mm transmurally adjacent to the ventricular cavities),infarct region, and the remaining bulk myocardium. The conductivity inthe endocardial regions may be allowed to vary up to about for example10 times that of bulk myocardium to account for the fast conduction ofthe Purkinje system, if not explicitly modeled. In infarcted or ischemicregions, conductivity may be isotropic, and the conductivity may beallowed to vary between about 10%-90% of that in the bulk myocardium.

FIG. 3A depicts a biventricular computational model 300 of the left andright ventricles 310, in accordance with some example implementations.The computational model 300 is illustrated as a series of finite statesof the left ventricle 310 with simulated myocardial voltage maps 320 a-d(collectively referred to as a fibrillatory mapping 320) of myocardialvoltage potentials in different locations. However, as described herein,the computational model 300 can be a three-dimensional model of the leftand right ventricles 310 with the fibrillatory source mapping 320 (e.g.,as a fourth dimension) moving around/across the surface of the leftventricle 310, in accordance with patterns indicative of VF. In someaspects, the fibrillatory source mapping 320 can be representative ofendocardial, EKG, and/or VCG data.

In some aspects, a source of VF can be identified based on thefibrillatory source mapping 320. For example, based upon thefibrillatory mapping 320, the computing system 110 can determine thatelectrical voltage indicative of VF rotates around a particularpoint/area of pro-arrhythmic substrate, which can be identified as arotor in this case (e.g., alternatively as focal activation in others),as indicated by the highlighted source 330, illustrated through thewhite sites in FIG. 3B. In some aspects, the source 330 can be alocation of diseased cardiac substrate.

In some implementations, the computational model 300 can be displayedvia a user interface, and can include animations (e.g., showing movementof the fibrillatory mapping 320 and/or strength/percentage of VF/AFcycles/size of the source 330). In some aspects, the computational model300 can be a 4D model and/or the fibrillatory source mapping 320 can beregarded as a fourth dimension. Although only four states of the leftventricle 310 and fibrillatory mapping 320 are illustrated, any numberof states is possible (e.g., up to infinity). In some implementations,information about the source 330 (e.g., location, frequency/percentageof activation at particular locations, mechanism, etc.) can be storedand/or displayed to a user.

Different levels of granularity in the determined location offibrillation sources are possible. For example, in some implementations,the fibrillation mechanism (e.g., rotors or focal sources), the specificchamber of the heart (e.g., left or right atrium or ventricle), and/or aregion (e.g., anterior LV) of the fibrillation source can be identified.However, in some implementations, a specific location may be identified,which can be estimated based upon where an observed fibrillation sourcespends a certain amount of time or emanates from. The accuracy ofidentifying the location of a source of VF/AF can be based on thesensitivity of observed VCG patterns, the robustness of computationalmodels from which a library of VCG patterns are generated, and/or thepower of machine-learning methods.

As noted above, computational models can be based upon and/or includeEKG data. FIG. 4A depicts a graph 410 of EKG data computed from acomputational model, in accordance with some example implementations. Insome aspects, this EKG data can be generated through the use of a12-lead EKG sensor device. Although examples herein refer to endocardialand/or EKG data, the data may additionally or alternatively compriseother types of electrical data, such as VCG data. For example, FIG. 4Bdepicts a VCG 420 model with VCG tracings from a lateral viewpoint and aVCG model 430 with tracings from a superior viewpoint, in accordancewith some example implementations. In some aspects, the black cardiacmodel located inside of the VCG tracings are just for perspective, andmay not form part of the VCG tracings 420, 430. In some implementations,the VCG models 420, 430 can be generated based upon EKG data.

As noted above, the VCG models 420, 430 can be temporospatial VCG modelsconstructed from a large number of realistic biventricular computationalmodels of VF/AF (e.g., the computational models in the model library 192and/or the patient-specific models 194). In some implementations, themodel library 192 can include models simulating rotors/focal sourcesfrom different anatomical cardiac segments. More or less locations arepossible, and in some aspects, the simulated locations can encompass themajority of potential source locations.

In some aspect, the coordinate axes of VCG data and/or the coordinateaxes of the corresponding computational model can be rotated until theyare correctly aligned with each other, as previously described withrespect to the data transformation algorithms 197 of FIG. 1.

FIG. 5A depicts a fibrillatory map 500 in a patient's left ventricle 510during VF, in accordance with some example implementations. Similar tothe fibrillatory source map 200 of FIG. 2A, the fibrillatory map 500 canbe indicative of a VF rotor at a source 520. For example, FIG. 5Bdepicts a fibrillatory source map 550 of a rotor, in accordance withsome example implementations. In some aspects, this map 550 can be aposterior view of a patient 560. As illustrated in the sectional view570 of the fibrillatory source map 550, there may be areas of scar(gray) tissue 580, borderzone (red, orange, yellow, green, and blue)tissue 585, and/or normal (purple) tissue 590 in the patient's leftventricle. In some aspects, the myocardial scar/borderzone tissue 580,585 can sustain VF, and therefore should be identified for ablationand/or external radiation therapy in order to reduce the futureprobability of arrhythmias for the patient 560. In some aspects, thisscar tissue 580 and/or borderzone tissue 585 can be regarded aspro-arrhythmic cardiac substrate.

In order to identify the source of VF/AF within a patient, thecomputational models described herein can be compared against dataobtained from the patient. In some aspects, the patient may know thatthey have VF/AF, but does not know the location of the source of theirVF/AF. At this point, the patient may wish to learn the exact origin oftheir VF/AF (e.g., how bad is the patient's VF/AF and/or their risk ofcardiac death), whether an implantable cardioverter-defibrillator (ICD)will be beneficial, whether ablation or surgery will be beneficial forpreventing future arrhythmia, what the risks of surgery are, etc. Usingthe subject matter described herein, the patient may be able to go to aphysician who takes non-invasive measurements of the patient, providesthe measurement data to a computing apparatus, and receives data tobetter address the patient's questions and concerns.

In some implementations, the non-invasive measurements can includeobtaining EKG data from the patient, such as through the use of a12-lead EKG sensor. For example, FIG. 6A depicts a graph 610 of EKGreadings during VF, in accordance with some example implementations. Insome aspects, the EKG data is obtained during induced VF/AF. Based uponthe EKG readings, VCG models and/or tracings can be generated for thepatient. For example, FIG. 6B depicts a VCG model 620 from a lateralviewpoint and a VCG model 630 from a superior viewpoint, in accordancewith some example implementations. In some aspects, the black cardiacmodel is just provided for orientation, and forms no part of the VCGmodels 620, 630 or the VCG tracings. In some implementations, the VCGmodels 620, 630 can be generated without the use of the EKG reading.

Once VCG models 620, 630 are obtained for the patient, they can becompared against VCG models stored in the VCG library 196, for example.Based upon the calculated similarities between the patient-derived VCGmodels 620, 630 and simulated/stored VCG models, VF/AF sources can beidentified. In some aspects, the similarities can be measured in one ormore (e.g., two) orthogonal planes. In some implementations, a Pearsoncorrelation or similar comparison algorithm can be used to compare VCGs.Based upon the correlation factor, a VF/AF source can be identifiedwithin a patient.

As a patient may have more than one VF/AF source, multiple comparisonscan be made. For example, a VCG can be obtained from a patient over aseries of time intervals. In some aspects, a VCG can be generated foreach time interval, which can be 1 ms in duration, for each VF cycle(which can be approximately 200 ms in duration). Each of the VF cycleVCG can be compared against stored VF cycle VCGs to identify which VCGcorrelates most with the patient's. A corresponding computational modelfrom the model library 192 can be identified based on whichever VCG hasthe highest correlation. The data associated with the identifiedcomputational model, including but not limited to VF source type,location, direction of rotation (if any), and/or the like as describedherein, can be regarded as representative of the patient's heart duringthe specific time interval. Based upon at least a portion of theidentified computational models, an estimated computational model can begenerated for the patient. In some implementations, a subset ofcomputational models of interest from the model library 192 can beidentified based upon the shape of the patient's heart, location ofscar, conduction properties, and/or the like (e.g., as determined by CT,MRI, echo, X-ray, fluoroscopy, or other imaging data). The identifiedset of computational models can be correlated with VCGs from the VCGlibrary 196, which can be the VCGs selected for comparison with thepatient.

Different rotor locations can create significantly different VCG loops.Therefore, the generation and comparison of patient-derived VCGs againstknown/estimated patterns for VF/AF sources can be an accurate method foridentifying VF/AF sources in patients.

In some implementations, a fibrillatory source map, such as thefibrillatory source map 280 of FIG. 2C and/or the fibrillatory sourcemap 290 of FIG. 2D, for illustrating the location of the VF/AF sourcecan be generated. For example, based upon comparing the VCGs, adetermination may be made as to how long a VF/AF source spends in one ormore locations. This temporal/percentage information can be used togenerate a fibrillatory source map, and/or the map can be overlaid onheart imaging data. Displaying the heart imaging data (e.g., a 3D model)with the fibrillatory source map can allow a medical professional toidentify additional characteristics about the VF/AF source(s), and/or totarget the source location for radiofrequency, cryogenic, ultrasound, orlaser ablation, external beam radiation, revascularization, genetransfer therapy, or other intervention to reduce future arrhythmiaburden.

In some implementations, additional computational models can begenerated based upon the comparison of patient-derived VCGs to storedVCGs. For example, one or more models can be identified from the modellibrary 192 that is most similar to the VF/AF source(s) identifiedwithin the patient. These one or more models can be combined to generatea composite model, which can include data representative of all VF/AFsources in the patient. Using the composite model, one VF/AF source canbe removed (e.g., removal is simulated) to generate a new model, whichcan be compared against the composite model to determine whether theremoval of each VF/AF source would be beneficial to the patient. If thechange in VF/AF exhibited between the composite model and the new modelis below a threshold value/percentage, then it can be determined thatthe removal of the VF/AF source may not be beneficial to the patient.The threshold value/percentage can vary depending upon the patient,medical professional, and/or other factors.

Although the measurements/comparisons described above are for apatient's left ventricle, measurements/comparisons can be performed in asimilar fashion, but with respect to a patient's right ventricle. Forexample, FIG. 7A depicts a computational model 700 of a right ventricle710, in accordance with some example implementations. As illustrated,the computational model 700 can include a plurality of myocardialvoltage maps 710 a-d, illustrated as a series of finite states, whichmay form a mapping of cardiac electrical activation in the rightventricle 710.

The computational model 700 can be based upon EKG data and/or can beused to generate EKG data. For example, FIG. 7B depicts a graph 730 ofEKG readings generated from the computational model, in accordance withsome example implementations. The process to create EKG readings can bethrough first computing VCGs from the electrical dipole of acomputational model for each time period. EKG tracings can then becomputed. For example, FIG. 7C depicts VCG models 740, 750, generatedfrom a cardiac model, in accordance with some example implementations.One or more VCG models, such as the VCG model 740 from a lateralviewpoint and/or the VCG model 750 from a vertical viewpoint arecomputed from the computational model. EKG tracings may be derived fromthis data thereafter. Importantly, the VCG models 740, 750, allowcomparison and matching to VCG data from a patient, as described herein.As illustrated, the VCGs models 740, 750 are color-coded based on timinginformation.

Although the measurements/comparisons described above are for apatient's left and right ventricles in VF, measurements/comparisons canbe performed in a similar fashion, but with respect to a patient's rightor left atria during AF. For example, FIG. 8A depicts a computationalmodel 800 of the left and right atria 810, in accordance with someexample implementations. As illustrated, the computational model 800 caninclude myocardial voltage maps 820 a-d, which can be representative ofand/or based on endocardial and/or EKG data. From the computationalmodel 800, the location of a source of AF can be identified (whitedots). For example, FIG. 8B depicts the computational model 800 of theright atrium 810 with an identified rotor substrate 830 (denoted bywhite dots), in accordance with some example implementations.

FIG. 9 illustrates an example computing apparatus 900 which may be usedto implement one or more of the described devices and/or components, inaccordance with some example implementations. For example, at least aportion of the computing apparatus 900 may be used to implement at leasta portion of the computing device 110, an apparatus providing thedatabase 120, an apparatus providing the external software 130, one ormore of the user access devices 140, one or more of the sensor devices150, and/or the access device 165. Computing apparatus 900 may performone or more of the processes described herein.

As illustrated, computing apparatus 900 may include one or moreprocessors such as processor 910 to execute instructions that mayimplement operations consistent with those described herein. Apparatus900 may include memory 920 to store executable instructions and/orinformation. Memory 920 may include solid-state memory, solid-state diskdrives, magnetic disk drives, or any other information storage device.In some aspects, the memory 920 may provide storage for at least aportion of a database (e.g., the database 120 or some other organizationof data). Apparatus 900 may include a network interface 940 to a wirednetwork or a wireless network, such as the network 160 of FIG. 1.Wireless networks may include WiFi, WiMax, and cellular networks(2G/3G/4G/5G), and/or any other wireless network. In order to effectuatewireless communications, the network interface 940, for example, mayutilize one or more antennas, such as antenna 980.

Apparatus 900 may include one or more user interface, such as userinterface 950. The user interface 950 can include hardware or softwareinterfaces, such as a keyboard, mouse, or other interface, some of whichmay include a touchscreen integrated with a display 930. The display 930may be used to display information such as promotional offers or currentinventory, provide prompts to a user, receive user input, and/or thelike. In various implementations, the user interface 950 can include oneor more peripheral devices and/or the user interface 950 may beconfigured to communicate with these peripheral devices.

In some aspects, the user interface 950 may include one or more of thesensors described herein and/or may include an interface to one or moreof the sensors described herein. The operation of these sensors may becontrolled at least in part by a sensor module 960. The apparatus 900may also comprise and input and output filter 970, which can filterinformation received from the sensors or other user interfaces, receivedand/or transmitted by the network interface, and/or the like. Forexample, signals detected through sensors can be passed through thefilter 970 for proper signal conditioning, and the filtered data maythen be passed to the microcontroller sensor module 960 and/or processor910 for validation and processing (e.g., before transmitting results oran indication via the network interface 940). The apparatus 900 may bepowered through the use of one or more power sources, such as powersource 990. As illustrated, one or more of the components of theapparatus 900 may communicate and/or receive power through a system bus999. In some aspects, the apparatus 900 may be regarded as at least aportion of a server or an apparatus which utilizes at least a portion ofa server.

FIG. 10 illustrates a flowchart of a method for computationallocalization of fibrillation sources, in accordance with some exampleimplementations. In various implementations, the method 1000 (or atleast a portion thereof) may be performed by one or more of thecomputing device 110, an apparatus providing the database 120, anapparatus providing the external software 130, one or more of the useraccess devices 140, one or more of the sensor devices 150, the accessdevice 165, the computing apparatus 900, other related apparatuses,and/or some portion thereof.

Method 1000 can start at operational block 1010 where the apparatus 900,for example, can generate heart models with varying shapes, scars, andfibrillation sources. Other aspects can additionally or alternatively beused, such as geometries, fiber orientations, fibrillation source types,fibrillation source locations, and/or the like.

Method 1000 can proceed to operational block 1020 where the apparatus900, for example, can generate vectorcardiogram plots based on the heartmodels. For example, the heart models can be used to compute model VCGsand/or generate a comprehensive VCG library.

Method 1000 can proceed to operational block 1030 where the apparatus900, for example, can filter heart models based on a patient's heartshape and/or scars. In some aspects, the filtering can be based oncomputerized tomography imaging data, magnetic resonance imaging data,echocardiography data, X-ray data, fluoroscopy data, and/or the like.

Method 1000 can proceed to operational block 1040 where the apparatus900, for example, can generate vectorcardiogram plots based on thepatient's heart.

Method 1000 can proceed to operational block 1050 where the apparatus900, for example, can compare patient's vectorcardiogram plots againstheart model vectorcardiogram plots. The vectorcardiogram plots can beretrieved, such as from a VCG library. In some implementations, a subsetof the vectorcardiogram plots in a VCG library can be selected basedupon the filtered heart models. The comparison can comprise determining,for each of the stored VCGs, a correlation factor between a generatedVCG and the stored VCGs, and at least one matched VCG can be identifiedas the stored VCG(s) with a highest correlation factor.

Method 1000 can proceed to operational block 1060 where the apparatus900, for example, can generate a mapping of fibrillation sources in thepatient's heart. In some implementations, a computational model caninclude a three-dimensional mesh in a heart shape and a finite statefibrillatory source map of cardiac electrical activations mapped to themesh. In some implementations, method 1000 can involve generatingelectrocardiogram plots based on the patient's heart, and/or generatingthe representation of electrical activation of the patient's heart basedon the electrocardiogram plots.

Method 1000 can proceed to operational block 1070 where the apparatus900, for example, can display mapping through a user interface (e.g.,user interface 950). The mapping can be displayed through the use ofheart models, which can include one or more of the left atrium, theright atrium, the ventricle, or the right ventricle. The models can betwo-dimensional, three-dimensional, four-dimensional, and/or may beanimated. In some aspects, displaying can enable guiding of an ablationprocedure using radiofrequency, cryogenics, ultrasound, laser, externalbeam radiation, and/or the like.

In some implementations, method 1000 can involve generating a pluralityof vectorcardiogram plots based on one or more stored computationalmodels, and/or generating one or more electrocardiogram plots based onthe plurality of vectorcardiogram plots.

Performance of the method 1000 and/or a portion thereof can allow foridentification of VF/AF sources with increased accuracy, decreasedinvasiveness, and/or decreased cost.

FIG. 11 illustrates a flowchart of a method for computationallocalization of fibrillation sources, in accordance with some exampleimplementations (e.g., similar to the illustrations in FIG. 12). Invarious implementations, the method 1000 (or at least a portion thereof)may be performed by one or more of the computing device 110, anapparatus providing the database 120, an apparatus providing theexternal software 130, one or more of the user access devices 140, oneor more of the sensor devices 150, the access device 165, the computingapparatus 900, other related apparatuses, and/or some portion thereof.

Method 1100 can start at operational block 1110 where the apparatus 900,for example, can determine source(s) of fibrillation in patient's heart.In some implementations, the identification of fibrillation sources canbe similar to identification procedures discussed with respect to themethod 1000 of FIG. 10.

Method 1100 can proceed to operational block 1120 where the apparatus900, for example, can generate heart model based on patient's heartshape, scars, and/or fibrillation source(s).

Method 1100 can proceed to operational block 1130 where the apparatus900, for example, can generate another heart model with at least onefibrillation source removed. In some aspects, a computational model canbe generated by removing one of the one or more fibrillation sourcesfrom an original computational model

Method 1100 can proceed to operational block 1140 where the apparatus900, for example, can determine a change in fibrillation in the anotherheart model.

Method 1100 can return to operational block 1130 if there are additionalsources to evaluate. For example, if three sources of VF/AF wereidentified, then the method 1100 can evaluate the removal of each of thesources, alone or in combination with any number of the other twosources (e.g., seven unique evaluations). Through this procedure, a user(e.g., medical professional) can identify a best course of action inremoving VF/AF sources.

Method 1100 can proceed to operational block 1150 where the apparatus900, for example, can display at least a portion of the heart modelsthrough a user interface (e.g., user interface 950). The heart modelscan include one or more of the left atrium, the right atrium, theventricle, or the right ventricle. The models can be two-dimensional,three-dimensional, four-dimensional, and/or may be animated. In someaspects, a side-by-side comparison of models with and without VF/AFsources can be provided. In some aspects, method 1100 can includedetermining a change in fibrillation between the new computationalmodel(s) and the original computational model(s). In some aspects, thedisplaying the side-by-side comparison can enables a targetedventricular fibrillation or ventricular fibrillation ablation therapy byproviding predictive ablation outcome data and/or selection of anoptimized ablation strategy.

Performance of the method 1100 and/or a portion thereof can allow fordeterminations of whether the elimination of each individual VF/AFsource may or may not benefit a patient. For example, if the change inVF/AF in a model where a particular source is removed is not decreasedby a threshold amount (e.g., percentage), then invasive proceduresremoving (or reducing the strength of) the particular source can beavoided.

Although several aspects are described herein with respect to VF and AF,other implementations are possible. For example, other arrhythmia orpatterns within humans and/or animals can be observed and/or a sourcethereof can be identified. If a source of an abnormal pattern within apatient is identified, then it may displayed (e.g., in two-dimensionalor three-dimensional modelling) to a user (e.g., a medical professional)such that the user can see inside of a patient's body without making anincision.

One or more aspects or features of the subject matter described hereincan be realized in digital electronic circuitry, integrated circuitry,specially designed application specific integrated circuits (ASICs),field programmable gate arrays (FPGAs) computer hardware, firmware,software, and/or combinations thereof. These various aspects or featurescan include implementation in one or more computer programs that areexecutable and/or interpretable on a programmable system including atleast one programmable processor, which can be special or generalpurpose, coupled to receive data and instructions from, and to transmitdata and instructions to, a storage system, at least one input device,and at least one output device. The programmable system or computingsystem may include clients and servers. A client and server aregenerally remote from each other and typically interact through acommunication network. The relationship of client and server arises byvirtue of computer programs running on the respective computers andhaving a client-server relationship to each other.

These computer programs, which can also be referred to as programs,software, software applications, applications, components, or code,include machine instructions for a programmable processor, and can beimplemented in a high-level procedural and/or object-orientedprogramming language, and/or in assembly/machine language. As usedherein, the term “machine-readable medium” refers to any computerprogram product, apparatus and/or device, such as for example magneticdiscs, optical disks, memory, and Programmable Logic Devices (PLDs),used to provide machine instructions and/or data to a programmableprocessor, including a machine-readable medium that receives machineinstructions as a machine-readable signal. The term “machine-readablesignal” refers to any signal used to provide machine instructions and/ordata to a programmable processor. The machine-readable medium can storesuch machine instructions non-transitorily, such as for example as woulda non-transient solid-state memory or a magnetic hard drive or anyequivalent storage medium. The machine-readable medium can alternativelyor additionally store such machine instructions in a transient manner,such as for example as would a processor cache or other random accessmemory associated with one or more physical processor cores.

To provide for interaction with a user, one or more aspects or featuresof the subject matter described herein can be implemented on a computerhaving a display device, such as for example a cathode ray tube (CRT) ora liquid crystal display (LCD) or a light emitting diode (LED) monitorfor displaying information to the user and a keyboard and a pointingdevice, such as for example a mouse or a trackball, by which the usermay provide input to the computer. Other kinds of devices can be used toprovide for interaction with a user as well. For example, feedbackprovided to the user can be any form of sensory feedback, such as forexample visual feedback, auditory feedback, or tactile feedback; andinput from the user may be received in any form, including acousticinput, speech input, tactile input, and/or the like. Other possibleinput devices include touch screens or other touch-sensitive devicessuch as single or multi-point resistive or capacitive trackpads, voicerecognition hardware and software, optical scanners, optical pointers,digital image capture devices and associated interpretation software,and the like.

The subject matter described herein can be embodied in systems,apparatus, methods, and/or articles depending on the desiredconfiguration. The implementations set forth in the foregoingdescription do not represent all implementations consistent with thesubject matter described herein. Instead, they are merely some examplesconsistent with aspects related to the described subject matter.Although a few variations have been described in detail above, othermodifications or additions are possible. In particular, further featuresand/or variations can be provided in addition to those set forth herein.For example, the implementations described above can be directed tovarious combinations and sub-combinations of the disclosed featuresand/or combinations and sub-combinations of several further featuresdisclosed above.

In the descriptions above and in the claims, phrases such as “at leastone of” or “one or more of” may occur followed by a conjunctive list ofelements or features. The term “and/or” may also occur in a list of twoor more elements or features. Unless otherwise implicitly or explicitlycontradicted by the context in which it is used, such phrases areintended to mean any of the listed elements or features individually orany of the recited elements or features in combination with any of theother recited elements or features. For example, the phrases “at leastone of A and B;” “one or more of A and B;” and “A and/or B” are eachintended to mean “A alone, B alone, or A and B together.” A similarinterpretation is also intended for lists including three or more items.For example, the phrases “at least one of A, B, and C;” “one or more ofA, B, and C;” and “A, B, and/or C” are each intended to mean “A alone, Balone, C alone, A and B together, A and C together, B and C together, orA and B and C together.” The use of the term “based on,” above and inthe claims is intended to mean “based at least in part on,” such that afeature or element that is not recited is also permissible.

The illustrated methods are exemplary only. Although the methods areillustrated as having a specific operational flow, two or moreoperations may be combined into a single operation, a single operationmay be performed in two or more separate operations, one or more of theillustrated operations may not be present in various implementations,and/or additional operations which are not illustrated may be part ofthe methods. In addition, the logic flows depicted in the accompanyingfigures and/or described herein do not necessarily require theparticular order shown, or sequential order, to achieve desirableresults. Other implementations may be within the scope of the followingclaims.

What is claimed is:
 1. A method performed by a computing system foridentifying a source location of an arrhythmia of a target patient basedon a patient-specific library of computational models of a heart, themethod comprising: for each of a plurality of patients, accessingcharacteristics of the heart of that patient, the characteristicsincluding a source location of an arrhythmia, the characteristics arenot derived from the target patient; generating a patient-specificcomputational model of the heart of that patient based on thecharacteristics of the heart of that patient, the patient-specificcomputational model including a three-dimensional mesh representing theheart; simulating electrical activations of the heart of that patient togenerate source maps of the electrical activations mapped to thethree-dimensional mesh; generating a representation of electricalactivations of the heart of that patient using the patient-specificcomputational model of the heart of that patient; and storing in thepatient-specific library the representation of the electricalactivations of that patient; and identifying a source location of anarrhythmia of the target patient who is not one of the plurality ofpatients based on comparison of a representation of electricalactivations of the heart of the target patient to representations ofelectrical activations stored in the patient-specific library.
 2. Themethod of claim 1 wherein the location is associated with a rotor of theheart.
 3. The method of claim 1 wherein the location is associated witha focal source of the heart.
 4. The method of claim 1 further comprisinggenerating, for at least some of the patients, multiple representationsof the electrical activations of the heart of that patient that arebased on different source locations of a possible arrhythmia.
 5. Themethod of claim 1 wherein the representation of the electricalactivations of the heart of a patient is a cardiogram.
 6. The method ofclaim 1 wherein a characteristic is an anatomical characteristic of theheart of a patient.
 7. The method of claim 1 wherein thepatient-specific library is stored in a cloud-based system.
 8. Themethod of claim 7 further comprising identifying the source location ofan arrhythmia of the target patient based on a representation of theelectrical activations of the heart of the target patient that isprovided to the cloud-based system via a gateway to the cloud-basedsystem.
 9. The method of claim 1 further comprising validating arepresentation of electrical activations generated using a computationalmodel generated based on a source location of an arrhythmia by comparingthat representation of electrical activations to a representation ofelectrical activations collected from a patient with the same sourcelocation of an arrhythmia.
 10. A computing system for generating alibrary of computational models for identifying a source location of anabnormal pattern within an organ of a target patient, the computingsystem comprising: one or more processors; and one or more memoriesstoring instructions, which when executed by the one or more processors,cause operations comprising, for each of a plurality of sets ofcharacteristics of the organ: running a simulation to generate acomputational model of the organ based on that set of characteristics ofthe organ, the characteristics including the source location of anabnormal pattern; generating a representation of electrical activationsof the organ using that computational model of the organ; and storing inthe library that representation of the electrical activations of theorgan and an indication of the source location of the set ofcharacteristics wherein the sets of characteristics are not derived fromthe target patient.
 11. The computing system of claim 10 wherein theorgan is a heart, the abnormal pattern is an arrhythmia, andrepresentation of the electrical activations of the heart is acardiogram.
 12. The computing system of claim 11 wherein a set ofcharacteristics is specific to a patient other than the target patientand the computational model generated based on that set ofcharacteristic is patient-specific.
 13. The computing system of claim 11wherein a set of characteristics is not specific to a patient.
 14. Thecomputing system of claim 11 wherein a characteristic is an anatomicalcharacteristic of the heart of a patient other than the target patient.15. The computing system of claim 11 wherein a computational model for apatient includes a three-dimensional mesh of the heart from which acardiogram is mapped and wherein the computational model is stored inthe library.
 16. The computing system of claim 11 wherein the library isstored in a cloud-based system.
 17. The computing system of claim 11wherein the library is stored in a cloud-based system and theinstructions further include instructions for identifying the locationof an arrhythmia of the target patient based on a cardiogram of thetarget patient that is provided to the cloud-based system via a gatewayto the cloud-based system, the location of the arrhythmia identifiedbased on comparison of cardiograms of the library to the cardiogram ofthe target patient.
 18. The computing system of claim 10 wherein thelibrary is stored in a cloud-based system and the instructions furtherinclude instructions for identifying the source location of the abnormalpattern within the organ of the target patient based on a representationof electrical activations that is provided to the cloud-based system viaa gateway to the cloud-based system, the location identified based oncomparison of the representations of the electrical activations of thelibrary to the representation of the electrical activations of thetarget patient.
 19. The computing system of claim 10 further includesinstructions for validating a representation of electrical activationsgenerated using a computational model generated based on a sourcelocation of an abnormal pattern by comparing that representation ofelectrical activations to a representation of electrical activationscollected from a patient with the same source location of an abnormalpattern.
 20. A computing system for identifying a possible sourcelocation of an arrhythmia within a heart of a target patient, thecomputing system comprising: one or more processors; and one or morememories storing instructions, which when executed by the one or moreprocessors, cause operations comprising: accessing a library thatincludes cardiograms associated with source locations of arrhythmiaswithin a heart wherein the cardiograms are generated based onsimulations of electrical activations of hearts based on characteristicsof the hearts that are not specific to the heart of the target patient,the characteristics of the hearts including a source location of anarrhythmia; receiving a cardiogram of the target patient; searching thelibrary for one or more cardiograms that match the cardiogram of thetarget patient; and designating as a possible source location for thetarget patient a source location associated with a matching cardiogram.21. The computing system of claim 20 wherein cardiograms are furtherassociated with physical characteristics of the heart and wherein theinstructions for searching further identifies a filtered set ofcardiograms of the library based on physical characteristics of thetarget patient matching the physical characteristics associated with thecardiograms and identifies one or more cardiograms of the filtered setthat match the cardiogram of the target patient.
 22. The computingsystem of claim 20 wherein the cardiograms of the library being derivedfrom computational models generated by the simulations based on sets ofcharacteristics of hearts.
 23. The computing system of claim 22 whereinthe computational models include patient-specific computational models.24. The computing system of claim 22 wherein the computational modelsinclude non-patient-specific computational models.
 25. The computingsystem of claim 22 wherein a characteristic is an anatomicalcharacteristic of the heart of the target patient.
 26. The computingsystem of claim 22 wherein the library includes, for each cardiogram, acomputational model from which the cardiogram is generated, acomputational model including a three-dimensional mesh generated basedon the physical characteristics of a heart and wherein computationalmodels are stored in the library with each cardiogram being associatedwith the computational model from which the cardiograms were generated.27. The computing system of claim 26 wherein the instructions furtherinclude instructions that run simulations to generate a computationalmodel with a source location.
 28. The computing system of claim 20wherein the library is stored in a cloud-based system.
 29. The computingsystem of claim 20 wherein the library is stored in a cloud-based systemand the instructions further include instructions for receiving thecardiogram of the target patient provided to the cloud-based system viaa gateway to the cloud-based system.
 30. The computing system of claim20 wherein the instructions further include instructions for outputtingan indication of the designated possible source location for guiding anablation procedure.
 31. The computing system of claim 20 wherein thelocation is associated with a rotor of the heart.
 32. The computingsystem of claim 20 wherein the location is associated with a focalsource of the heart.
 33. A method performed by one or more computingsystems for identifying a possible location of an abnormal patternwithin an organ of a target patient, the method comprising: accessing alibrary that includes representations of electrical activations of theorgan, the representations generated based on simulations of electricalactivations of the organ and based on locations of abnormal patternswithin the organ, the representations not including a representationgenerated based on the organ of the target patient; receiving arepresentation of electrical activations of the organ of the targetpatient; searching the library for one or more representations ofelectrical activations that match the representation of the electricalactivations of the target patient; and designating as the possiblelocation of an abnormal pattern for the target patient the locationassociated with a matching representation of electrical activations. 34.The method of claim 33 wherein the organ is a heart, the representationof electrical activations are cardiograms, and the abnormal pattern isan arrhythmia.
 35. The method of claim 34 wherein the cardiograms arefurther associated with the cardiograms with physical characteristics ofthe heart and wherein the searching further comprises identifying afiltered set of cardiograms of the library based on physicalcharacteristics of the target patient matching the physicalcharacteristics associated with the cardiograms and identifies one ormore cardiograms of the filtered set that match the cardiogram of thetarget patient.
 36. The method of claim 33 wherein the representationsof the electrical activations are derived from computational modelsgenerated by the simulations based on sets of characteristics of theorgan.
 37. The method of claim 33 wherein library is stored in acloud-based system and further comprising receiving the representationof the electrical activations of the organ of the target patient via agateway to the cloud-based system.
 38. The method of claim 33 furthercomprising outputting an indication of the designated possible sourcelocation for guiding a medical procedure.
 39. The method of claim 38wherein the medical procedure is an ablation procedure.
 40. The methodof claim 33 further comprising generating a computational model that isnot based on the possible location and simulating electrical activationsof the organ based on the computational model to indicate effects ofremoval of the abnormal pattern.
 41. The method of claim 40 wherein thecomputational model is a composite model generated from multiplecomputational models.